Estimating latent infections

A retrospective


Ryan J. Tibshirani, Rachel Lobay, and Daniel J. McDonald

and CMU’s Delphi Group…

CDC Flu — December 18, 2023

Goal

Use reported cases to estimate actual infections

  • For every US state
  • Between March 1, 2020 to January 1, 2022
  • Provide an “authoritative” estimate with uncertainty
  • No compartmental models, no sampling or Bayesian methods

Retrospective deconvolution

  • Based on prior work (Jahja et al. 2021)
  • Take reported cases and deconvolve them to find when symptoms began
  • Private CDC linelist to estimate the delay from symptom onset to case report
    • Different delay distribution for every report date and state
  • Combine with Literature estimate of the delay from infection to symptom onset
    • Variant specific
    • Prevailing variant mix taken from GISAID
  • Convolve both to get delay distribution from infection to case report

Callouts

Note

You can use these. See https://quarto.org/docs/authoring/callouts.html

Final slide

Thanks:

  • The whole CMU Delphi Team (across many institutions)
  • Optum/UnitedHealthcare, Change Healthcare.
  • Google, Facebook, Amazon Web Services.
  • Quidel, SafeGraph, Qualtrics.
  • Centers for Disease Control and Prevention.
  • Council of State and Territorial Epidemiologists

Latent Infections — cmu-delphi.github.io/cdcflu-latent-infections